In this lecture, we learned how to calculate performance metrics for our models.
We will look at you loose at these places on record and you'll see off auto secrets.
We have already discussed about this in order to relax chips.
We are not going to see all too calculated and Biton.
You can always calculate this values manually using positive groo, negative, false positive from your
confusion matrix.
What do you going to also find it?
Using a single line of code?
So from Escalon Metrics, we will import precisionist score and record they scored.
Now, to calculate preachiness score, we just have to write Bridgit on this scores.
This is the function and then we have to write two arguments.
First one is the crucial value of Y.
And the second one is the predicted value of five.
So we run the score.
The president for our largest sigmoidal is zero point six five three.
Similarly, to calculate the recall, we just have to right.
Recall underscored his score.
And then again, actual value of play and credit value of where you can see the recall for our largest
sigmoidal is zero point six six.
To calculate the U.S., we have to import ardency, underscore, AUC underscore is scored from Escalon
metrics.
When you do that.
And then to calculate the U.S. score, we just have to write our policy, underscore, AUC, underscore,
rescored and then the actual value of Y and the value of Y.
And this.
So the U.S. for what logistic regression model is zero point six eight five.
We will also see you see skorts for Aldea and Gay and then.
And we will use this EOC scores to compare the accuracy of our models in the later part.
Of course.